initial push

.gitignore

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+# Virtual environments
+venv

app.py

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+import gradio as gr
+import requests
+from PIL import Image, ImageDraw
+from transformers import AutoProcessor, AutoModelForCausalLM
+from io import BytesIO
+import torch
+
+# Set device
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+# Load model and processor
+model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large", torch_dtype=torch_dtype, trust_remote_code=True).to(device)
+processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True)
+
+# List of colors to cycle through for bounding boxes
+COLORS = ["red", "blue", "green", "yellow", "purple", "orange", "cyan", "magenta"]
+
+# Prediction function
+def predict_from_url(url):
+    prompt = "<OD>"
+    if not url:
+        return {"Error": "Please input a URL"}, None
+
+    try:
+        image = Image.open(BytesIO(requests.get(url).content))
+    except Exception as e:
+        return {"Error": f"Failed to load image: {str(e)}"}, None
+    
+    inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype)
+    generated_ids = model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=4096,
+        num_beams=3,
+        do_sample=False
+    )
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    parsed_answer = processor.post_process_generation(generated_text, task=prompt, image_size=(image.width, image.height))
+
+    labels = parsed_answer.get('<OD>', {}).get('labels', [])
+    bboxes = parsed_answer.get('<OD>', {}).get('bboxes', [])
+    
+    # Draw bounding boxes on the image
+    draw = ImageDraw.Draw(image)
+    legend = []  # Store legend entries
+    for idx, (bbox, label) in enumerate(zip(bboxes, labels)):
+        x1, y1, x2, y2 = bbox
+        color = COLORS[idx % len(COLORS)]  # Cycle through colors
+        draw.rectangle([x1, y1, x2, y2], outline=color, width=3)
+        legend.append(f"{label}: {color}")
+    
+
+    return "\n".join(legend), image
+
+# Gradio interface
+demo = gr.Interface(
+    fn=predict_from_url, 
+    inputs=gr.Textbox(label="Enter Image URL"),
+    outputs=[
+        gr.Textbox(label="Legend"),  # Output the legend
+        gr.Image(label="Image with Bounding Boxes")  # Output the processed image
+    ],
+    title="Item Classifier with Bounding Boxes and Legend",
+    allow_flagging="never"
+)
+
+# Launch the interface
+demo.launch()

requirements.txt

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+transformers
+torch
+requests
+Pillow
+open_clip_torch
+ftfy
+einops
+
+# This is only needed for local deployment
+gradio